Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

查看全文 作　　者：Christopher [1]T.Nelson;Ayana [1,2]Ghosh;Mark [1]Oxley;Xiaohang [3]Zhang;Maxim [1,2]Ziatdinov;Ichiro [3]Takeuchi;Sergei [1]V.Kalinin

机构地区：[1]Center for Nanophase Materials Sciences,Oak Ridge National Laboratory,Oak Ridge,TN,USA;[2]Computational Sciences and Engineering Division,Oak Ridge National Laboratory,Oak Ridge,TN,USA;[3]Department of Materials Science and Engineering,University of Maryland,College Park,MD,USA

出　　处：《npj Computational Materials》索引2021年第1期,共11页

基　　金：This STEM effort is based upon work supported by the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences(BES),Materials Sciences and Engineering Division(S.V.K.,C.T.N.).This ML effort is based upon work supported by the U.S.DOE,Office of Science,Office of Basic Energy Sciences Data,Artificial Intelligence and Machine Learning at DOE Scientific User Facilities(A.G.).The work was performed and partially supported(M.Z.)at Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences(CNMS),a U.S.DOE,Office of Science User Facility.The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301 and the Center for Spintronic Materials in Advanced Information Technologies(SMART)one of the centers in nCORE,a Semiconductor Research Corporation(SRC)program sponsored by NSF and NIST.The authors gratefully acknowledge Dr.Karren More(CNMS)for careful reading and editing the manuscript. 摘　　要：Over the last decade,scanning transmission electron microscopy(STEM)has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision,opening the pathway toward exploring ferroelectric,ferroelastic,and chemical phenomena on the atomic scale.Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements.Here,we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks(DCNNs).In this approach,the DCNN is trained on the labeled part of the image(i.e.,for human labelling),and the trained network is subsequently applied to other images.We explore the effects of the choice of the descriptors(centered on atomic columns and grid-based),the effects of observational bias,and whether the network trained on one composition can be applied to a different one.This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations. 关键词：DEEP network FERROELECTRIC

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Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

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